Systems thinking asks
scientists to look at the bigger picture and take a more holistic approach to
their work. Systems thinkers consider how their field of study might relate to
other disciplines, and how their research might inform or benefit from the broader
knowledge stream.

In modern medicinal
chemistry, systems thinking has become an absolute necessity due to the nature
of the drug development process. Chemists, biologists, doctors, mathematicians,
physicists and other scientists work collaboratively through partnerships and
initiatives that combine the resources of academia, government, research
organizations, hospitals and commercial business—all in a combined effort to
develop effective new medicines.

This means systems
thinking is critical for the pharmaceutical industry. To thrive, pharma
companies can’t just stay insulated and try to “do their own thing” in the same
way that they always have. Welcoming new collaborators, integrating new systems
and adopting new technologies are all part of embracing the systems thinking
approach.

In my upcoming webinar, titled “Chemistry Data for Systems Thinkers,” I will discuss how to take a system approach to creating new chemistry knowledge, how to translate knowledge into useful applications, and how to be prepared to take on our current global crisis.

Among the relevant
topics that will be included:

Systematic integration of biological and chemical data

AI-ready data for synthesis route designs and prediction

Best practices for the pharma industry and education system

Practical examples of using system thinking (e.g. digitization of chemistry knowledge in pharma, data readiness in green chemistry, responding to COVID-19 using conscientious data excerption from literature)

While mounting piles of data and information silos can make it a challenge to embrace a systems thinking approach to pharma, it is fast becoming a necessity to do so. Companies that adopt the mindset, strategies and tools that enable big-picture thinking will be better able to innovate and succeed.

A computer virus exploits the operating
system of a computer to replicate (copy itself) and send copies of itself to other
computers in the network. In the same manner, a human virus manipulates the
cell’s “operating system,” managed by cellular proteins, to replicate and infect
other cells in the body. Specifically, the virus forces the cell to terminate
its ongoing operations and start making copies of its viral particles by controlling
the expression and behavior of pre-existing proteins in the cell.

SARS-CoV-2, the coronavirus responsible
for COVID-19 disease, is very new. Yet, the MERS and SARS epidemics have
generated sufficient scientific knowledge about coronaviruses, which can be
useful to understand the biological mechanisms by which SARS-CoV-2 can control
human cells.

Text mining for knowledge extraction

With this in mind, we used Elsevier’s
text mining tool to identify human proteins that have been shown by scientists
to be increased or decreased by coronaviruses after infection. Similarly, we searched
to identify cellular processes that are increased or decreased by certain
chemical compounds (drugs), such as FDA-approved drugs, to revert the efforts
made by coronaviruses at controlling cellular processes during infections. This
is equivalent to scanning the scientific literature for computer processes that
are turned on (increased) or off (decreased) by computer viruses and
antiviruses.

We gathered the information and stored it in the form of a database that displays the links between studied drugs, proteins and diseases (MERS and SARS). To allow for continuous access to up-to-date information, such as drug side effects, we ensured that drugs, proteins and diseases in our database are linked to external identifiers in reviewed databases such as HGNC and Uniprot databases. We also preserved the provenance of the extracted information by providing a link to the original PubMed identifiers.

Knowledge graph for data explorers

At this point, we decided to use a graph database to explore the data, which now looks like a directed graph. The dataset is now freely accessible for download and exploration on Mendeley. Having noticed this, our partner Neo4J has loaded the data into a hosted instance of neo4j, which can be accessed using the following credentials:

username: elsevier

password: 3153v13ruser

It would be helpful to familiarize yourself with the neo4j query language, cypher, to explore this data. Here is a useful reference card that you can use.

Coronavirus
Research Hub for data scientists

Recognizing the needs of gathering and exploring data in support of COVID-19 drugs and vaccines development, Elsevier has launched the Coronavirus Research Hub with a particular focus on data science. In addition to the original dataset of this visualized virus regulation pathway, the portal also includes the access to COVID-19 related full text articles in ScienceDirect, and more than 14 million cross-publisher full text articles plus the CORD-19 Dataset. Join the research hub today!

]]>https://pharma.elsevier.com/covid-19/navigating-the-virus-regulation-pathway-through-text-mining-and-knowledge-graph/feed/0Global networks form to take on the problem of drug-induced liver injury (DILI)https://pharma.elsevier.com/pharma-rd/global-networks-form-to-take-on-the-problem-of-drug-induced-liver-injury-dili/
https://pharma.elsevier.com/pharma-rd/global-networks-form-to-take-on-the-problem-of-drug-induced-liver-injury-dili/#respondWed, 20 May 2020 15:32:04 +0000https://pharma.elsevier.com/?p=10083

Drug-induced liver injury (DILI), which is one of the top causes of liver failure in the United States [1], is an adverse reaction that can be caused by a significant number of medications. DILI can be fatal and is not only a major concern post-market, but also during drug development as it is the top safety-related reason for late-stage clinical trial failure.

The international
pharmaceutical research community has been working together to help take on the
problem of drug-induced liver injury by establishing networks, consortia and
partnerships designed to understand more about DILI.

For example, the Drug-Induced Liver Injury Network (DILIN) was formed by the National Institute of Diabetes and Digestive and Kidney Diseases with the aim of identifying and analyzing severe liver injury cases that were caused by both prescription and over-the-counter drugs, as well as alternative and herbal medicines. The efforts of DILIN’s experts is to determine the degree of impact that each medication has on liver injury, which would allow pharmaceutical companies to improve their DILI risk assessment, especially in patients taking multiple drugs.

Currently DILIN is
working on two noteworthy studies to:

1) establish a
registry of people who have experienced liver injury in the past 10 years using
drugs or herbal / dietary supplements, and

2) establish a registry of people who have experienced liver injury in the past
six months after using certain drugs or alternative products.

The Spanish DILI Registry began in 1994, establishing a network of clinician-pharmacists and hepatologists to identify cases of liver damage caused by drugs. Since then, the organization has sought to share its methodologies and expand its international reach further by creating the Spanish-Latin American DILI Network (SLATINDILI), which includes Argentina, Brazil, Chile, Ecuador, Mexico, Paraguay, Peru and Venezuela.

In 2014, they went on to start a European network called the Pro-Euro DILI Registry, which has since been working on enabling the development and implementation of novel safety biomarkers in clinical trials and diagnosis of disease.

China, too, is collecting important data and creating a powerful DILI resource with Hepatox.org, a clinical database, application and information platform focused on DILI. Researchers can look there to find the latest information and resources relevant to DILI, medical liver injury cases, an online DILI evaluation tool, research registration, a management system for clinical trials, and more.

The IQ-DILI Initiative, affiliated with the International Consortium for Innovation and Quality in Pharmaceutical Development, is another organization taking on DILI. Their mission is to define best practices for the detection, monitoring, management and prevention of DILI in clinical trials and pharmacovigilance programs.

As we discussed in an earlier post, even Elsevier has gotten involved, partnering with the FDA to work on developing a model to predict DILI. Similarly, the DILI-sim Initiative, a partnership that includes the Hamner Institutes for Health Sciences, has the goal of developing a computational model that can predict if a new drug candidate could cause drug-induced liver injury.

With so much of the
global pharma community banding together, we hope that it won’t be long until
DILI is better understood by researchers, and eventually easier to avoid or
prevent in drug development and beyond.

With the trend of open science, people are becoming more receptive
to the idea of sharing data and using what is already accessible. Particularly
for researchers, there is so much data available that can answer questions and
develop hypotheses and strategies that could save effort, time and money. This
is mainly important in cases where rapid discoveries are required, such as in
the case of the current COVID-19 outbreak.

Recently, a research group in China approached us to help answer four questions they had in mind concerning covid-19:

Are there any key proteins used by Coronavirinae for entry and fusion into host cells, which could be common with the COVID-19 virus?

Are there available inhibitors of the current COVID-19 receptor, ACE2, which could be used for potential drug screening?

Are there any COVID-19 Spike protein inhibitors for potential drug screening?

Are there any immune-modulators used in anti-virus therapies that could be potential drugs for Systemic Inflammatory Response Syndrome (SIRS), a major player in multiple-organ damage and malfunction during late-stage COVID-19 infection.

To address these questions, we used Elsevier Life Sciences solutions to retrieve literature, compounds, biological pathways and key information pertinent to these questions. Specifically, our team used Embase and Elsevier Text Mining tools to retrieve information from literature, at the sentence-evidence level, concerning coronaviruses entry/fusion proteins and their potential host cells. Reaxys was also used to identify potential ACE2 and Spike protein inhibitors that could be potentially used for drug screening. Finally, Embase, PharmaPendium, Pathway Studio and ETM were used to find potential inflammation pathways that could be associated with late-stage COVID-19 infection and the relevant immune-modulators that have been used for the treatment of viral infections.

Preliminary analysis of the retrieved information provided primary
answers to the client’s four questions, which bring new insights into modulators,
biomarkers and potential treatments for COVID-19 infection. Compared with other
coronaviruses, COVID-19 has distinct spike proteins and four insertions in the
spike glycoprotein gene that are critical for the virus to enter the target
cells. Of importance, our data showed a huge difference not only in the
inflammatory markers between COVID-19-infected patients and healthy individuals
but also between Intensive care unit (ICU) patients and non-ICU patients.

Interestingly,
non-survivors of 2019-nCoV infection showed a continuous increase in neutrophil
count, D-dimer, blood urea, and creatinine levels and a continuous decrease in lymphocyte
counts until death occurred. As for the pathophysiology, inhibition of ACE2 by
COVID-19 infection may lead to the induction NADPH oxidase, and hence, to an
increase in the production of Reactive Oxygen Species (ROS), major inflammatory
mediators. Therefore, evidence from literature indicates that treatment of
COVID-19-related injury could be achieved by balancing the renin-angiotensin
system (RAS), a complex of alternative enzymatic pathways in which ACE2 plays a
major role as an anti-inflammatory mediator1.

While this COVID-19 information package was particularly developed
to answer these four specific questions, it can also be used to address other
queries related to COVID-19 and other coronaviruses.

Feel free to contact me if you are performing research on coronaviruses and you are interested in this COVID-19 information package. I would also be happy to help you find answers to your specific research questions.

Take a look at this infographic demonstrating how leveraging Entellect and collaborating with three partners led to a datathon that delivered four potential repurposing candidates in just 60 days.

]]>https://pharma.elsevier.com/pharma-rd/partnering-to-identify-repurposing-candidates-for-chronic-pancreatitis/feed/0Reaxys database search now available from Marvinhttps://pharma.elsevier.com/chemistry/reaxys-database-search-now-available-from-marvin/
https://pharma.elsevier.com/chemistry/reaxys-database-search-now-available-from-marvin/#respondFri, 08 May 2020 08:30:19 +0000https://pharma.elsevier.com/?p=10063

The
number of chemical reactions and substances platforms like Reaxys has grown
exponentially in recent years. The challenge for chemical information tools has
shifted from exploration of new horizons in the chemistry space to refining and
filtering structure and reaction searches to find the exact substances or
reactions that one is seeking. As the next step in a long-term collaboration,
Reaxys is now connected with ChemAxon’s flagship cheminformatics solution
Marvin, making complex structure searches faster and easier, and directly
accessible from the desktop application.

In
the early days of Reaxys, MarvinSketch was chosen as web component editor to
supplement searches. When the new Reaxys interface was released, the updated
Marvin JS version was included as a default drawing tool in continued success. It allows academic and industry users to access the structure
editor from within Reaxys to search the database, and simplifies access to a
range of commonly used features. Marvin JS has an optimized user interface so
that users can draw simple and complex query structures intuitively in Reaxys.

This
greatly simplifies user workflows, speeding up the chemistry information
research process by allowing researchers to initiate a Reaxys search from
within the MarvinSketch desktop interface, and then to be taken to the related
search results within Reaxys. Researchers can search for substances and
reactions in Reaxys all with a simple click from MarvinSketch and find more
information and innovation in less time—no more cut and paste between
applications.

“Analyzing
user workflows, we found that the scientists using desktop tools would benefit
from day-to-day connection and the next step simply became obvious to both
companies. With Reaxys search available immediately from MarvinSketch, we are
excited to further empower scientific research and discovery by enhancing the
scope of what users can achieve in our desktop application. This functionality
is markedly improving our users’ daily workflow, gives momentum to further
scientific discoveries and generates new platforms for research,” said Efi
Hoffmann, Product Manager, Marvin JS, at ChemAxon.

“The
combination of two such outstanding tools allows our users access to the best
of two worlds simultaneously, searching Elsevier’s extensive database from our
desktop editor swiftly and directly.”

If you would like to get more details about how Reaxys can help you, visit us here.

Learn more about ChemAxon

ChemAxon
is a chemical and biological software development company that provides
solutions for the biotechnology and pharmaceutical industries; successfully
used in publishing, flavors, fragrances, petroleum and fine chemicals research
as well. ChemAxon’s entire product portfolio offers out-of-the-box solutions
for scientists, back-end tools for IT professionals, components to add extra
functionality, and integrations to make our technology available from 3rd party
software like Microsoft Excel or KNIME. The company began in 1998 with the
development of toolkits – flagship chemical editor (Marvin), and leading chemical
search engines (JChem), which quickly became the top-of-mind enterprise
solutions in several industries where chemistry is involved. Our technology is
capable of adding chemical intelligence to the most common relational databases
(MySQL, PostgreSQL, Oracle Cartridge etc.) and provide rapid hit return even in
extremely large databases of chemical structures.

The number of vaccines in the pipeline to battle the severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has reached to
>70 candidates [1,2]. Many different vaccination strategies are being
tested, even a noncoronavirus-specific candidate (Bacille Calmette-Guerin
vaccine) [2,3] is being investigated because of its potential to promote a less
inflammatory immune response to respiratory pathogens [2]. However, a SARS-Cov-2–specific
vaccine will likely be a better strategy.

Vector Vaccines

The candidate developed by CanSino Biologics Inc and Beijing
Institute of Biotechnology (Ad5-nCoV) is the furthest along in the process (approaching
phase 2 trials) [4]. These developers are using the same nonreplicating adenovirus
(AdV) vector they used to develop their approved Ebola vaccine [5]. The AdV5
vector, often not used because of extensive preexisting immunity in humans [6],
showed previous utility in clinical trials when inoculated at high dose (~1010
virions) [7,8]. The immune response to this vaccine was strong but short lived
and probably requires a 6-month booster. Although Ad5-EBOV is approved in China
for vaccination [5], it might be premature to consider this vector a viable vaccine
platform. Because the Ebola outbreak ended before phase 3 trials could begin,
this vaccine was not fully vetted.

However, there are 14 more SARS-CoV-2 vaccine candidates that are virus vectors: five are other AdVs (one being a chimp AdV), three are measles viruses, two are influenza viruses, three are poxviruses (one being a horsepox virus), and one is a vesicular stomatitis virus [1]. There is also one bacteria vector (Bifidobacterium longum) called bacTRL-Spike that produces SARS-CoV-2 antigens with its plasmid DNA [9].

Many of these candidates carry the risk of immune destruction before the SARS-CoV-2 immune response adequately develops. The vaccine candidates at less risk are the chimp AdV vector, human AdV26 vector, poxvirus vectors, VSV vector, and bacterial vector (this bacteria is part of our natural flora and gets administered orally). The bacteria vector and the chimp AdV (ChAdOx1) are approaching clinical trials [9,10]. Similar to Ad5-nCov, the ChAdOx1 vaccine (COV001) is also a nonreplicating vector that will be given in a single shot at a similar high dose.

Protein-Based Vaccines

There are also a large number of protein-based vaccine candidates (~29) [1,2]. The candidate furthest along in clinical trials is the one made by Shenzhen Geno-Immune Medical Institute (Covid-19 aAPC) [11]. Its strategy involves using a lentivirus to construct artificial antigen-presenting cells (APCs) to present antigens and then inactivating these cells proliferative capacity. Covid-19 aAPC presents structural and nonstructural SARS-CoV-2 antigens and is administered in three doses.

Another protein-based vaccine candidate is University of Queensland’s spike peptide frozen into prefusion conformation via a molecular clamp. This strategy potentially promotes a strong neutralizing antibody response, but earlier studies showed this technology induced a robust antibody response that was not neutralizing [12].

In case these other vaccines don’t work, we have more traditional vaccine candidates in the pipeline (two live attenuated and three killed) [1]. However, these types often take longer to get approved. By the end of the year, we’ll probably be at a better position to determine which vaccines are the better candidates.

Are you involved in COVID-19 vaccine development? To empower your further exploration, Elsevier has launched the Coronavirus Research Hub, aimed to provide you, as an individual researcher, free access to a selection of Elsevier content and services through 28th October 2020. Visit the Research Hub and join your fellow researchers to bring this crisis to an end.

Check out a special infographic we created to show how Elsevier’s Professional Services team can support your COVID-19 research.

]]>https://pharma.elsevier.com/covid-19/infographic-elseviers-professional-services-team-provides-covid-19-insights/feed/0Nucleic Acid–Based Vaccines in Development for SARS-CoV-2https://pharma.elsevier.com/covid-19/nucleic-acid-based-vaccines-in-development-for-sars-cov-2/
https://pharma.elsevier.com/covid-19/nucleic-acid-based-vaccines-in-development-for-sars-cov-2/#respondFri, 01 May 2020 08:30:29 +0000https://pharma.elsevier.com/?p=10021

With the coronavirus disease 2019 (COVID-19) case and death
count climbing [1] despite multiple industry shutdowns and extensive social
distancing efforts, it has become clear that a vaccine is required to control the
spread of the COVID-19 causative agent, severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2). Many
different researchers, institutes and companies specializing in vaccine
development across the world have entered the race and announced a candidate,
resulting in an astonishingly large number (>70) of vaccines in the pipeline
[2,3].

This
post discusses the nucleic acid vaccine candidates being tested, and a future
post will discuss the other SARS-CoV-2 vaccine candidates (predominantly virus
vectors and protein-based vaccines).

Many are advocating for a nucleic acid­–based vaccine to win
the SARS-CoV-2 vaccine race. As of yet, no RNA or DNA vaccine has been approved
for human use, but the technology has been improving over the past few years,
and multiple DNA vaccines have been approved for use in animals [4].

Perfecting the RNA and DNA vaccine development strategy will
be an important innovation going forward. With the extent of globalization and
the increased frequency of epidemic and pandemic scares, vaccine development needs
to occur much faster (than the average 10 years [5]) to protect us from highly
transmittable infectious pathogens. RNA and DNA vaccines provide us the
potential for more rapid vaccine development because synthetic RNA and DNA are
easier to construct and purify from contaminants (improving safety) and scale
up to large volume than traditional vaccines.

Because we are in the early stages of SARS-CoV-2 vaccine development, the complete vaccine platform has not been decided on by all developers, but as of April 17, 2020, approximately six are DNA and approximately 12 are RNA vaccines [2,3].

DNA Vaccines

With the DNA vaccines, developers are testing different entry mechanisms: needle injection plus electroporation (Takis, Karolinska Institute and Inovio Pharmaceuticals) and needle-free systems (Osaka University and Immunomic Therapeutic) [2,6,7]. The vaccines administered by the two different needle-free platforms (ActranzaTM lab and PharmaJet Tropis Needle-Free Injector System) and the Inovio Pharmaceutical candidate will be injected intradermally [8], and the Takis and Karolinska Institute DNA vaccine candidates will be administered by the intramuscular route [6].

Most DNA vaccine developers have not revealed which genes they are administering in the vaccine, but Zydus Cadila has stated they are using membrane protein [2]. Of the SARS-CoV-2 DNA vaccines, the Immunomic Therapeutics candidate is the most unique; gene sequences will be chosen on the basis of their predicted ability to stimulate a strong immune response (selected for in collaboration with EpiVax) and will be ligated to the lysosomal-associated membrane protein gene [9].

RNA Vaccines

The design of the RNA vaccines is more uncertain. Most of the delivery platforms have been stated or are likely to be lipid nanoparticles (LNPs) [2]. However, BioNTech has three different lipid delivery platforms (lipoplexes, LNPs and polyplexes) [10] and has not stated which one they are using. The delivery platform that Arcturus Therapeutics will use, the LUNAR system of Synthetic Genomics, is supposed to be widely applicable for multiple inoculation routes and target tissues [11]. However, few developers have stated their inoculation route or the tissue they are targeting their LNPs to [12]. Two developers (Arcturus Therapeutics, Imperial College London) are using self-replicating mRNA [2,12], two (Translate Bio, Curevac) are using unmodified optimized mRNA sequences [13,14], and one (BioNTech) is currently still testing its three different RNA formats [15]. Many companies are likely to use the major structural protein (spike) as the gene of choice, but not all developers have explicitly said so [12].

Despite this early stage of development, one RNA vaccine (mRNA-1273, made through a National Institute of Allergy and Infectious Diseases and Moderna partnership) and one DNA vaccine (INO-4800, made by Inovio Pharmaceuticals) are already in phase 1 clinical trials, attesting to the greater speed of nucleic acid vaccine development. Both of these developers have previous experience working on coronavirus vaccines [16,17].

There is still a long road ahead for these vaccine
developers, but I’m betting one of these nucleic acid­–based vaccines will win
the SARS-CoV-2 vaccine race.

Are you involved in COVID-19 vaccine development? To empower your further exploration, Elsevier has launched the Coronavirus Research Hub, aimed to provide you, as an individual researcher, free access to a selection of Elsevier content and services through 28th October 2020. Visit the Research Hub and join your fellow researchers to bring this crisis to an end.

]]>https://pharma.elsevier.com/covid-19/nucleic-acid-based-vaccines-in-development-for-sars-cov-2/feed/0Mathematical modeling the emergence and spread of new pathogens: Insight for SARS-CoV-2 and other similar viruseshttps://pharma.elsevier.com/covid-19/mathematical-modeling-the-emergence-and-spread-of-new-pathogens-insight-for-sars-cov-2-and-other-similar-viruses/
https://pharma.elsevier.com/covid-19/mathematical-modeling-the-emergence-and-spread-of-new-pathogens-insight-for-sars-cov-2-and-other-similar-viruses/#respondFri, 24 Apr 2020 08:30:03 +0000https://pharma.elsevier.com/?p=9955

Knowing if and how rapidly an emerging pathogen will spread
through a population enables public health officials to make well-informed
decisions to protect the public. Mathematical modeling can provide them this
means to predict pathogen spread, but modeling previously unheard of pathogens,
like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is
challenging.

Typically, mathematical modeling requires researchers to acquire
at least one dataset with the relevant data points to develop the model, and
another similar dataset to validate the model. For emerging diseases like novel
coronavirus disease 2019 (COVID-19), for which we did not have a readily
available diagnostic kit to distinguish SARS-CoV-2–positive cases from negative
cases, validity and completeness of the data is often problematic.

Mathematical modeling
strategies

Designing a model involves researchers making presumptions
about which parameters influence pathogen transmission the most and thus which
variables should be included in modeling and need inferences made about them
[1,2]. Researchers designing models may assume all people are equally
susceptible to the pathogen and equal mixing of the population, along with some
other assumptions such as infinite population size (to avoid modeling births
and deaths) to make modeling easier. However, many researchers idealize realism-type
approaches and value the addition of more parameters.

Modelers may incorporate into their model variables for
population age structure and growth, different infection susceptibilities based
on age (or another factor), and social networking patterns. However, the more
parameters included, the more mathematically complex the model becomes. Complex
models might be more realistic, but they are often not better. With larger
numbers of variables, missing or inaccurate data points have more influence
over model results, and longer time periods are needed to compute outcomes.
Also, sometimes variables seem important but have little influence over model results.

Models developed for pathogens during their emergence are
often inaccurate [3]. Typically, when incomplete datasets are used to develop
models, multiple different models appear capable of fitting the existing data
points but predict different outcomes.

COVID-19 findings

For the COVID-19 pandemic, mathematical modeling has been
used to estimate a few aspects relating to pathogen spread, such as the basic
reproductive number (R0, number of secondary infections caused by 1
infection in a completely susceptible population) for SARS-CoV-2 (R0
2.8-4.0) [4] and the percentage of people with asymptomatic infections (~17.9%)
[5]. Some modeling studies have shown population control measures did decrease
pathogen spread [6,7], and Kucharski et al. found that four independent
SARS-CoV-2 introduction events into environments mimicking Wuhan, China would
provide >50% chance of virus establishment in that population [7]. As of
March 29, 2020, no studies published in scientific journals have shown
predictions on the extent of pathogen spread globally.

Modeling spread of
pandemic

In future efforts to model pandemic spread of SARS-CoV-2, I would
suggest testing a model designed against another RNA virus (or a virus with a similar
mutation potential) that had an established surveillance system ongoing
(relatively valid data set) and caused a respiratory disease (similar
transmission capability) in a population that was arguably 100% susceptible:
maybe the H5N1 or H1N1 pandemic strains. One could perhaps take a model
designed to predict pandemic spread of a somewhat similar pathogen and plug-in another
dataset. If the model predicts COVID-19 spread, perhaps we can use this model
with the next respiratory disease pandemic.